# Lake Kinneret Multi-Sensor Limnological Dataset (2017-2023)

## Overview

This repository contains a comprehensive 7-year (2017-2023) multi-sensor limnological monitoring dataset for Lake Kinneret (Sea of Galilee), Israel's primary freshwater reservoir. The dataset integrates five complementary data sources:

1. In-situ fluoroprobe measurements (167,754 observations) - Phytoplankton spectral fluorescence from 5 monitoring stations
2. Laboratory chlorophyll-a measurements (153 samples) - Independent reference data from Station A
3. Satellite-derived chlorophyll-a (269 Sentinel-2 scenes) - Spatially explicit retrievals at 30 m resolution
4. Meteorological observations (346,953 records) - Continuous 10-minute data from Ginosar station
5. Machine learning predictions (567,320 predictions) - SVR model predictions of phytoplankton functional groups

This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

## Citation

Please cite this dataset as:

Tal, O., Gal, G., and Amitai, Y. (2026). Lake Kinneret Multi-Sensor Limnological Dataset (2017-2023).
https://doi.org/10.7910/DVN/62PMQS

Associated manuscript:

Tal, O., Gal, G., and Amitai, Y. (2026). Identifying phytoplankton dynamics in a monomictic lake
ecosystem by multi-sensor monitoring integrating fluoroprobe, meteorological and satellite-derived
observations. Earth System Science Data, [in review].

## Study Site

Lake Kinneret (Sea of Galilee), Israel

- Location: 32.8 N, 35.6 E (Northern Israel)
- Elevation: ~209 m below sea level
- Surface area: ~166 km2
- Maximum depth: ~42 m (mean: ~24 m)
- Volume: ~4 km3
- Lake type: Warm monomictic subtropical lake

Monitoring stations:
- Station A: 32.82130 N, 35.58904 E (Central basin, deepest point ~42 m)
- Station D: 32.72154 N, 35.59321 E (Southern basin, 1 km from southern shore)
- Station G: 32.86941 N, 35.60568 E (Northern basin, Jordan River inflow)
- Station H: 32.86031 N, 35.54378 E (Northwestern basin, western shore)
- Station K: 32.76221 N, 35.57173 E (Central-western basin)

Meteorological station:
- Ginosar: 32.84814 N, 35.52837 E (Western shore, 10 m above the pier)

## Temporal Coverage

- Overall period: January 2017 - December 2023
- Fluoroprobe: 2017-05-21 to 2023-12-31 (295 sampling dates, 5 stations)
- Laboratory chlorophyll: 2017-2023 (153 samples, Station A)
- Satellite chlorophyll: 2017-01-01 to 2023-12-31 (269 scenes, cloud dependent)
- Meteorology: 2017-01-01 to 2023-09-27 (346,953 records, 10-minute intervals)
- ML predictions: 2017-05-21 to 2023-12-31 (295 dates, aligned with fluoroprobe sampling)

## Repository Structure

kinneret-multisensor-2017-2024/
  README.txt                                    # This file
  METADATA.txt                                  # Detailed technical metadata
  METADATA_ALL_DATASETS.csv                     # Tabular metadata for all datasets
  DATA_DICTIONARY.csv                           # Variable definitions for all files
  fluoroprobe/
    fluoroprobe_2017_2023.csv                   # 175,774 observations (raw)
    fluoroprobe_2017_2023_clean.csv             # 167,754 observations (quality controlled)
  laboratory_chlorophyll/
    laboratory_chlorophyll_2017-2023.csv         # 153 samples (raw)
    laboratory_chlorophyll_2017-2023_clean.csv   # Quality controlled version
  satellite_chlorophyll/
    S2_YYYY_*_chl.csv                           # Annual gridded chlorophyll (7 files, raw)
    S2_YYYY_*_chl_clean.csv                     # Annual gridded chlorophyll (7 files, QC)
    satellite_chl_by_station_clean.csv          # Station-aggregated data
  meteorology/
    meteorology_ginosar_2017-2023.csv           # 346,953 records, 10-min intervals
  ml_predictions/
    phytoplankton_predictions_2017_2023.csv     # 567,320 predictions
  validation/
    fluoroprobe_validation_matchups_clean.csv   # 394 satellite-fluoroprobe matchups
    fluoroprobe_validation_statistics_clean.csv # Per-station validation statistics
    seasonal_validation_statistics_clean.csv    # Seasonal validation statistics
    station_seasonal_validation_statistics_clean.csv
    overall_validation_statistics.csv           # Overall validation metrics
    laboratory_fluoroprobe_matchups.csv         # 133 lab-fluoroprobe matchups
    laboratory_satellite_matchups.csv           # 68 lab-satellite matchups
  examples/
    load_and_plot_examples.py                   # Python usage example

## Data Files Description

### 1. Fluoroprobe Data (In-situ Phytoplankton)

Files: fluoroprobe/fluoroprobe_2017_2023.csv (raw), fluoroprobe_2017_2023_clean.csv (QC)

Description: Spectrofluorometric vertical profiles from a bbe Moldaenke FluoroProbe III
measuring phytoplankton spectral fluorescence at 5 monitoring stations.

Key columns: date, station, depth, LED fluorescence signals at 6 wavelengths (370, 470, 525,
570, 590, 610 nm), water temperature, transmission, yellow substances, pressure.

Observations: 175,774 raw / 167,754 clean, across 295 sampling dates (5 stations: A, D, G, H, K)
Temporal resolution: Weekly (weather dependent)
Vertical resolution: ~0.2 m depth intervals

### 2. Laboratory Chlorophyll-a Data

Files: laboratory_chlorophyll/laboratory_chlorophyll_2017-2023.csv (raw and clean versions)

Description: Laboratory-measured chlorophyll-a concentrations from water samples at Station A.
Chlorophyll-a determined fluorometrically following Holm-Hansen et al. (1965) after extraction
in 90% acetone.

Columns: date, station, depth, chlorophyll (ug/l)
Observations: 153 samples spanning 2017-2023
Depth range: Surface layer (0-2 m)

### 3. Satellite Chlorophyll-a Data

Files: satellite_chlorophyll/S2_YYYY_*_chl.csv (7 annual files, raw and clean versions)

Description: Chlorophyll-a concentrations derived from Sentinel-2 MultiSpectral Instrument (MSI)
imagery using the OC2 algorithm (blue/green band ratio, 492/560 nm) after atmospheric correction
with ACOLITE (version 20210802.0; Vanhellemont & Ruddick, 2018).

Columns: date, depth (0 m surface), lon, lat, chl (ug/L)
Scenes: 269 unique dates across 2017-2023
Spatial resolution: 30 m (resampled)
Land buffer: 600 m from shoreline excluded

Validation against fluoroprobe (depth-integrated 0-10 m):
- 394 matchups across 5 stations (A: 101, D: 70, G: 95, H: 80, K: 48)
- R2 = 0.347, RMSE = 8.59 ug/L, Bias = -5.12 ug/L
- Extraction radius: 110 m (Stations A, G, H, K), 750 m (Station D)

Validation against laboratory chlorophyll (surface 0-2 m, Station A):
- 68 matchups, R2 = 0.073, RMSE = 12.41 ug/L

### 4. Meteorological Data

File: meteorology/meteorology_ginosar_2017-2023.csv

Description: Continuous meteorological observations from the Ginosar offshore station,
10 m above the pier. Data collected using Campbell Scientific CR1000 data loggers.

Sensors:
- Air temperature: Young 43372C
- Relative humidity: Rotronic HygroMet MP
- Wind speed/direction: Young 05306 anemometer
- Shortwave radiation: Kipp & Zonen CM11 pyranometer
- Longwave radiation: Kipp & Zonen CG1 pyrgeometer

Records: 346,953 (10-minute intervals), 2017-01-01 to 2023-09-27
Data completeness: >95% for all variables

### 5. Machine Learning Predictions

File: ml_predictions/phytoplankton_predictions_2017_2023.csv

Description: Support Vector Regression (SVR) model predictions of phytoplankton functional
group biomass, based on methodology from Lafer et al. (2025). Model trained on 6 years
(2017-2022) of fluoroprobe measurements paired with microscope-based phytoplankton counts.

Columns: station, date, year, month, week, depth, group_num, group_name, predicted_biomass_ug_ml

Predicted groups:
1. Green Algae (Chlorophyta) - R2 = 0.61
2. Cyanobacteria (Cyanophyta) - R2 = 0.46
3. Diatoms (Bacillariophyceae) - R2 = 0.36
4. Dinoflagellates (Dinophyceae) - R2 = 0.55
5. Cryptophyta - R2 = 0.27

Observations: 567,320 predictions across 91,454 unique station-date-depth combinations
Units: ug/mL (wet weight biomass)

### 6. Validation Data

Directory: validation/

Contains satellite-fluoroprobe matchups (n=394), per-station and seasonal validation
statistics, overall validation metrics, and laboratory cross-validation matchups
(lab-fluoroprobe: n=133, lab-satellite: n=68).

## Data Format Specifications

All data files use CSV (Comma-Separated Values) format:
- Encoding: UTF-8
- Delimiter: Comma (,)
- Decimal separator: Period (.)
- Date/time format: ISO 8601 (YYYY-MM-DD HH:MM:SS or YYYY-MM-DD)
- Missing values: Empty cells (read as NaN by Pandas and similar software)
- Header row: First row contains column names
- Coordinate system: WGS84 (EPSG:4326)

## Quality Control

Fluoroprobe: Regular sensor calibration by IOLR. Negative fluorescence values retained
(below detection limit). Transmission <50% flagged for turbidity interference.

Satellite: Atmospheric correction via ACOLITE. Negative reflectance values excluded.
Matchups with <3 valid pixels excluded. IQR outlier removal on regression residuals.

Meteorology: Automated range checks. >95% data completeness.

ML predictions: Model trained on 2017-2022 data with microscopy reference. K-fold
cross-validation. No extrapolation beyond observed environmental conditions.

For datasets with quality control (fluoroprobe, laboratory chlorophyll, satellite), both
original and quality-controlled versions are provided (denoted with 'clean' suffix).

## Column Definitions

See DATA_DICTIONARY.csv for complete variable definitions, units, and measurement methods.
See METADATA_ALL_DATASETS.csv for tabular metadata across all datasets.

## Authors and Contributions

Tal, O.: Conceptualization, Data curation, Formal analysis, Methodology, Software,
Validation, Visualization, Writing - original draft, Writing - review & editing.
Gal, G.: Funding acquisition, Writing - review & editing.
Amitai, Y.: Data curation, Investigation, Methodology, Validation, Writing - review & editing.

## Funding

Israel Water Authority (ongoing Lake Kinneret monitoring program)

## Acknowledgments

We thank the Kinneret Limnological Institute (IOLR) for long-term monitoring infrastructure
and institutional support. We thank Yuri Lechinsky for technical assistance with field
sampling and laboratory analyses. Sentinel-2 satellite imagery provided by the European
Space Agency (ESA) Copernicus Programme.

## Contact

Ofir Tal
Israel Oceanographic and Limnological Research (IOLR)
ofir.tal@ocean.org.il

## References

Lafer, D., Sukenik, A., Zohary, T., & Tal, O. (2025). Improving fluoroprobe sensor
performance through machine learning. Ecological Indicators, 170, 112983.

Vanhellemont, Q., & Ruddick, K. (2018). Atmospheric correction of metre-scale optical
satellite data for inland and coastal water applications. Remote Sensing of Environment,
216, 586-597.

Holm-Hansen, O., Lorenzen, C. J., Holmes, R. W., & Strickland, J. D. (1965). Fluorometric
determination of chlorophyll. ICES Journal of Marine Science, 30(1), 3-15.

## License

Creative Commons Attribution 4.0 International (CC BY 4.0)
https://creativecommons.org/licenses/by/4.0/


